16,439 research outputs found
Techniques for improving the labelling process of sentiment analysis in the Saudi stock market
Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" (Arabic source), "losses" (Arabic source), "green colour" (Arabic source:Arabic source), "growing" (Arabic source), "distribution" (Arabic source), "decrease" (Arabic source), "financial penalty" (Arabic source), and "delay" (Arabic source). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%
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Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach
Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.Comment: 11 pages, to appear at ACM MM'1
Seminar Users in the Arabic Twitter Sphere
We introduce the notion of "seminar users", who are social media users
engaged in propaganda in support of a political entity. We develop a framework
that can identify such users with 84.4% precision and 76.1% recall. While our
dataset is from the Arab region, omitting language-specific features has only a
minor impact on classification performance, and thus, our approach could work
for detecting seminar users in other parts of the world and in other languages.
We further explored a controversial political topic to observe the prevalence
and potential potency of such users. In our case study, we found that 25% of
the users engaged in the topic are in fact seminar users and their tweets make
nearly a third of the on-topic tweets. Moreover, they are often successful in
affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
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